A Distributed Adaptive Network Framework for Multi-Channel EEG Classification Using ERP Detection

Understanding brain function is one of the most challenging areas in brain signal processing. This study introduces a novel framework for electroencephalography (EEG) signal classification based on distributed adaptive networks using diffusion strategy. \color{green} Our approach models the brain as a multitask network, where EEG electrodes are considered as nodes within the network. The network parameters are dynamically optimized based on the data from the nodes and inter-node cooperation. \color{black} The proposed framework, which comprises network modeling and diffusion-based adaptation using the adapt then combine (ATC) algorithm, has been validated on synthetic, simulated, and real EEG datasets. Experimental results indicate that the proposed framework outperforms common methods in classifying EEG data based on event-related potential (ERP) \color{green} detection\color{black}, particularly in scenarios where machine learning-based models struggle with limited data. Furthermore, its ability to adapt to the non-stationary and dynamic nature of EEG signals and its efficient real-time implementation make this approach ideal for brain-computer interface (BCI), cognitive neuroscience, and clinical applications.